The Pharmacogenomics Knowledgebase (PharmGKB) is a resource that collects, curates, and disseminates information about the impact of human genetic variation on drug responses. It provides clinically relevant information, including dosing guidelines, annotated drug labels, and potentially actionable gene–drug associations and genotype–phenotype relationships. Curators assign levels of evidence to variant–drug associations using well-defined criteria based on careful literature review. Thus, PharmGKB is a useful source of high-quality information supporting personalized medicine–implementation projects.

We have conducted a genome screen of autism, by linkage analysis in an initial set of 90 multiplex sibships, with parents, containing 97 independent affected sib pairs (ASPs), with follow-up in 49 additional multiplex sibships, containing 50 ASPs. In total, 519 markers were genotyped, including 362 for the initial screen, and an additional 157 were genotyped in the follow-up. As a control, we also included in the analysis unaffected sibs, which provided 51 discordant sib pairs (DSPs) for the initial screen and 29 for the follow-up. In the initial phase of the work, we observed increased identity by descent (IBD) in the ASPs (sharing of 51.6%) compared with the DSPs (sharing of 50.8%). The excess sharing in the ASPs could not be attributed to the effect of a small number of loci but, rather, was due to the modest increase in the entire distribution of IBD. These results are most compatible with a model specifying a large number of loci (perhaps >/=15) and are less compatible with models specifying

Background The cost of genomic information has fallen steeply but the path to clinical translation of risk estimates for common variants found in genome wide association studies remains unclear. Since the speed and cost of sequencing complete genomes is rapidly declining, more comprehensive means of analyzing these data in concert with rare variants for genetic risk assessment and individualisation of therapy are required. Here, we present the first integrated analysis of a complete human genome in a clinical context. Methods An individual with a family history of vascular disease and early sudden death was evaluated. Clinical assessment included risk prediction for coronary artery disease, screening for causes of sudden cardiac death, and genetic counselling. Genetic analysis included the development of novel methods for the integration of whole genome sequence data including 2.6 million single nucleotide polymorphisms and 752 copy number variations. The algorithm focused on predicting genetic risk of genes associated with known Mendelian disease, recognised drug responses, and pathogenicity for novel variants. In addition, since integration of risk ratios derived from case control studies is challenging, we estimated posterior probabilities from age and sex appropriate prior probability and likelihood ratios derived for each genotype. In addition, we developed a visualisation approach to account for gene-environment interactions and conditionally dependent risks. Findings We found increased genetic risk for myocardial infarction, type II diabetes and certain cancers. Rare variants in LPA are consistent with the family history of coronary artery disease. Pharmacogenomic analysis suggested a positive response to lipid lowering therapy, likely clopidogrel resistance, and a low initial dosing requirement for warfarin. Many variants of uncertain significance were reported. Interpretation Although challenges remain, our results suggest that whole genome sequencing can yield useful and clinically relevant information for individual patients, especially for those with a strong family history of significant disease.

Accuracy of evolutionary analysis of populations within a species requires the testing of a large number of genetic polymorphisms belonging to many loci. We report here a reconstruction of human differentiation based on 100 DNA polymorphisms tested in five populations from four continents. The results agree with earlier conclusions based on other classes of genetic markers but reveal that Europeans do not fit a simple model of independently evolving populations with equal evolutionary rates. Evolutionary models involving early admixture are compatible with the data. Taking one such model into account, we examined through simulation whether random genetic drift alone might explain the variation among gene frequencies across populations and genes. A measure of variation among populations was calculated for each polymorphism, and its distribution for the 100 polymorphisms was compared with that expected for a drift-only hypothesis. At least two-thirds of the polymorphisms appear to be selectively neutral, but there are significant deviations at the two ends of the observed distribution of the measure of variation: a slight excess of polymorphisms with low variation and a greater excess with high variation. This indicates that a few DNA polymorphisms are affected by natural selection, rarely heterotic, and more often disruptive, while most are selectively neutral. This paper presents results of the first phase of our study of human evolution based on nuclear DNA variation in five populations studied for a selected set of 100 DNA polymorphisms. At this stage we have chosen to focus on a large number of markers typed on each of a small number of defined populations because the reliability of conclusions is so dependent on the number of independent markers (1-3). Using these data (presented in detail in ref. 38), we examine the shape of the tree and compare it with earlier trees, investigate an observed anomaly of evolutionary rates, and study variation of different genes and its bearing on the theory of neutral evolution.Prior studies of human populations using DNA markers have been limited almost entirely to mitochondrial DNA (4,